Different applied median filter in salt and pepper noise

Many impulse noise (IN) reduction methods suffer from two obstacles, the improper noise detectors and imperfect filters they used. To address such issue, in this paper, a weighted couple sparse representation model is presented to remove IN. In the proposed model, the complicated relationships between the reconstructed and the noisy images are exploited to make the coding coefficients more appropriate to recover the noise-free image. Moreover, the image pixels are classified into clear, slightly corrupted, and heavily corrupted ones. Different data-fidelity regularizations are then accordingly applied to different pixels to further improve the denoising performance. In our proposed method, the dictionary is directly trained on the noisy raw data by addressing a weighted rank-one minimization problem, which can capture more features of the original data. Experimental results demonstrate that the proposed method is superior to several state-of-the-art denoising methods.

Weighted Couple Sparse Representation With Classified Regularization for Impulse Noise Removal

A comprehensive survey on impulse and Gaussian denoising filters for digital images

In this paper, we deliver a new method to remove salt and pepper noise, which we refer to as based on pixel density filter (BPDF) The first step of the method is to determine whether or not a pixel is noisy, and then we decide on an adaptive window size that accepts the noisy pixel as the center The most repetitive noiseless pixel value within the window is set as the new pixel value By using 18 test images, we give the results of peak signal-to-noise ratio (PSNR), structural similarity (SSIM), image enhancement factor (IEF), standard median filter (SMF), adaptive median filter (AMF), adaptive fuzzy filter (AFM), progressive switching median filter (PSMF), decision-based algorithm (DBA), modified decision-based unsymmetrical trimmed median filter (MDBUTMF), noise adaptive fuzzy switching median filter (NAFSM), and BPDF The results show that BPDF produces better results than the above-mentioned methods at low and medium noise density

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A new method based on pixel density in salt and pepper noise removal

Impulse noise removal using SVM classification based fuzzy filter from gray scale images

A modified decision based unsymmetrical trimmed median filter algorithm for the restoration of gray scale, and color images that are highly corrupted by salt and pepper noise is proposed in this paper. The proposed algorithm replaces the noisy pixel by trimmed median value when other pixel values, 0's and 255's are present in the selected window and when all the pixel values are 0's and 255's then the noise pixel is replaced by mean value of all the elements present in the selected window. This proposed algorithm shows better results than the Standard Median Filter (MF), Decision Based Algorithm (DBA), Modified Decision Based Algorithm (MDBA), and Progressive Switched Median Filter (PSMF). The proposed algorithm is tested against different grayscale and color images and it gives better Peak Signal-to-Noise Ratio (PSNR) and Image Enhancement Factor (IEF).

Adabala N Subramanyam

Papers

Removal of High Density Salt and Pepper Noise Through Modified Decision Based Unsymmetric Trimmed Median Filter